23 lines
1,022 B
Markdown
23 lines
1,022 B
Markdown
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# News Summarizer
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This example goes through a series of steps:
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1. You choose a topic area (e.g., "news", "NVidia", "music", etc.).
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2. Gets the most recent articles on that topic from various sources.
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3. Uses Ollama to summarize each article.
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4. Creates chunks of sentences from each article.
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5. Uses Sentence Transformers to generate embeddings for each of those chunks.
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6. You enter a question regarding the summaries shown.
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7. Uses Sentence Transformers to generate an embedding for that question.
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8. Uses the embedded question to find the most similar chunks.
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9. Feeds all that to Ollama to generate a good answer to your question based on these news articles.
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This example lets you pick from a few different topic areas, then summarize the most recent x articles for that topic. It then creates chunks of sentences from each article and then generates embeddings for each of those chunks.
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You can run the example like this:
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```bash
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python3 -m pip install -r requirements.txt
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python3 summ.py
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```
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